With the advancement in language modeling, research on adversarial images has recently shifted from image classifiers to vision-language models (VLMs). Despite the new, text-prompt-driven nature of VLMs, we find that existing VLM adversaries have largely overlooked the dynamics of text prompts. In a typical use case, adversarial images can protect visual privacy by rejecting VLMs from answering questions about the image. However, existing protections are prompt-agnostic, i.e., indiscriminately rejecting any question, including those for potential legitimate use of the image. To address this limitation, we propose Prompt-Adapted (ProAda) adversarial images, which are optimized to balance the rejection of harmful prompts and the correct response of legitimate prompts. In particular, to achieve a more accurate adversarial direction, the text prompt is also iteratively updated with a simple classifier judging whether the harmfulness of the prompt remains. Extensive experiments on visual-privacy-related datasets validate that ProAda can reject over 90% of harmful prompts while allowing over 90% of legitimate ones, demonstrating superior overall performance compared to existing prompt-agnostic VLM adversaries. In addition, our ProAda achieves competitive overall cross-model transferability and improved robustness against countermeasures.
Figures & tables
Figure 1: Adversarial perturbations can protect VLMs from answering text prompts about the image content. However, existing prompt-agnostic protections Luo et al. (2024) ; Yang et al. (2024) ; Mittal et al. (2025) indiscriminately reject all kinds of prompts. In contrast, our prompt-Adapted method rejects harmful but accepts general prompts, providing a balance between privacy protection and image utility.
Figure 2: Framework of our Prompt-Adapted (ProAda) adversary against VLMs. The optimization of visual perturbation δv is conducted on both harmful prompts (for visual privacy protection) and general prompts (for maintaining general utility). Specifically, the response to general prompts is minimized towards the correct answer using Lg , and the response to harmful prompts is minimized towards a pre-defined target text (e.g., “unknown”) using Lh . In particular, optimization on harmful prompts also involves updating the text perturbation δt to achieve a more accurate adversarial direction, as commonly done in related work Luo et al. (2024) ; Lu et al. (2023) ; Zhang et al. (2024) ; Jia et al. (2025) . Here, δt is updated only when the adversarial text is judged to remain harmful.
Method
Untargeted SRh
Targeted SRh
SRg
Average
CroPA (Harmful) Mittal et al. (2025)
92.5
91.3
55.3
79.7
CroPA (All) Mittal et al. (2025)
94.0
93.8
43.1
77.0
Multi-P (Harmful) Luo et al. (2024)
90.9
84.0
82.0
85.6
Multi-P (All) Luo et al. (2024)
92.2
88.5
77.8
86.2
ITA Cai et al. (2025)
56.0
51.6
78.5
62.0
Our ProAda
93.1
93.0
92.4
92.8
Table 1: Untargeted and targeted success rates (SRs) of different methods.
Figure 3: Targeted success rates of our ProAda vs. CroPA over iterations. Our ProAda achieves substantially higher SRg and comparable targeted SRh .
Model/Method
InstructBLIP
LLaVA-1.5
MiniGPT-4
Overall
CroPA (Harmful)
46.9/96.2
13.7/94.8
37.2/81.0
61.6
CroPA (All)
48.6/95.1
10.7/95.7
39.3/82.0
61.9
Multi-P (Harmful)
40.0/96.5
9.5/95.8
41.6/79.8
60.5
Multi-P (All)
44.7/97.0
9.8/96.4
41.6/82.5
62.0
ITA
41.6/97.0
10.6/95.2
30.5/89.0
60.6
Our ProAda
53.8/98.0
10.4/96.8
37.3/86.6
63.8
Table 2: Cross-model transferability (untargeted SRh / SRg ) of different methods to mainstream VLMs.
Method
Untargeted SRh
Targeted SRh
SRg
Average
ProAda (default)
53.8
9.6
98.0
53.8
ProAda w/ BSR
44.2
0.4
97.8
47.5
ProAda w/ DI
45.4
11.1
95.9
50.8
ProAda w/ MI
38.9
7.1
98.3
48.1
ProAda w/ TI
48.8
2.5
97.9
49.7
ProAda w/ Logit
50.5
17.3
94.8
54.2
Table 3: Cross-model transferability of ProAda integrated with different transfer techniques.
Figure 4: Visualizations of the adversarial images crafted by our ProAda, along with the harmful and general prompts. Additional examples are provided in Figure A.1 of Appendix A.2 .
Method
Metric
w/o
Rotation
Gaussian
Bit-Depth
Noise
Brightness
CroPA (Harmful)
SR h
91.3
51.8
74.0
73.2
70.9
74.0
SR g
55.3
64.9
59.0
59.0
59.1
58.1
Average
73.3
58.4
66.5
66.1
65.0
66.1
CroPA (All)
SR h
93.8
52.9
75.0
77.9
69.3
75.0
SR g
43.1
62.7
48.0
51.6
50.4
51.8
Average
68.5
57.8
61.5
64.8
59.9
63.4
Table 4: Robustness of our ProAda vs. CroPA against five countermeasures. Here, only targeted SR h are reported, while untargeted SR h are all 100% because the transformations have substantially changed the original responses.
Method
SRh
SRg
Average
w/o SVM-based harmfulness judgment
71.5
61.7
66.6
w/o text perturbation update
65.0
89.1
77.1
Our ProAda ( Rh:g=1 )
80.6
95.5
88.1
Table 5: Ablation studies on the two main modules of ProAda.
Figure 10
Figure 7: Ablation studies on the hyper-parameters Rv:t and Rh:g for our ProAda method.
Targeted response
SRh
SRg
Average
“maybe"
65.0
89.1
77.1
“none"
87.5
75.0
81.3
“I am sorry"
98.5
97.3
97.9
“unspeakable"
77.5
65.7
71.6
“metaphor"
35.5
80.0
57.8
“0"
99.0
98.7
98.9
Table 6: ProAda optimized with different target response T on harmful prompts.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure A.1: Additional visualizations of the adversarial images crafted by ProAda, along with the harmful and general prompts.
Figure A.2: Success rates of ProAda over iterations under untargeted optimization.
Model
Untargeted SRh
Targeted SRh
SRg
Average
BLIP-2
93.1
93.0
92.4
92.8
LLaVA
100.0
98.3
77.6
92.0
Appendix
Table A.1: White-box success of ProAda on LLaVA-1.5.
Metric
Untargeted SRh
Targeted SRh
SRg
Overall
Value
93.1±9.33e−5
93.0 ±6.18e−5
92.4 ±2.35e−5
92.8 ±4.69e−5
Appendix
Table A.2: Our results are stable over 10 randomly sampled subsets.
Adversarial attacks on vision-language models exploit images, text, and multimodal interactions to manipulate outputs, bypass safety, or degrade reliability.